Many problems modeled with partial differential equations include parameters which need to be varied, e.g., to find an optimal choice of parameter values w.r.t. some cost functional.
Due to the large costs of repeatedly solving the problem for different parameters in such workflows, Model Order Reduction (MOR) techniques are often used to replace the model at hand by a quickly evaluable surrogate.
In this work we consider Reduced Basis (RB) methods, which build such a Reduced Order Model (ROM) by projecting the model equations onto a problem adapted subspace spanned by solutions of the Full Order Model (FOM) for appropriately selected snapshot parameters.
For a detailed overview and discussion on RB methods, we refer to the monographs [1]}, [2]} and the tutorial introduction [3]}.
